论文标题
自distillation增强组织病理学图像分类的蒙面自动编码器
Self-distillation Augmented Masked Autoencoders for Histopathological Image Classification
论文作者
论文摘要
近年来，自我监督学习（SSL）在组织病理学图像分析中引起了人们的关注。与对对比的学习相比，这与虚假负面问题有关，即，将语义相似的图像选为负样本，从生成范式中选择了蒙版的自动编码器（MAE）构建SSL，这可能是更合适的预训练。在本文中，我们介绍了MAE并验证可见斑块对组织病理学图像理解的影响。此外，提出了一种新型的SD-MAE模型，以实现自我验证增强的MAE。除了掩盖图像贴片上的重建损失外，SD-MAE还对可见斑块施加了自distillation损失，以增强位于浅层层的编码器的表示能力。我们将SD-MAE应用于组织病理学图像分类，细胞分割和对象检测。实验表明，与这些任务中的其他SSL方法相比，SD-MAE表现出竞争激烈的性能。
Self-supervised learning (SSL) has drawn increasing attention in histopathological image analysis in recent years. Compared to contrastive learning which is troubled with the false negative problem, i.e., semantically similar images are selected as negative samples, masked autoencoders (MAE) building SSL from a generative paradigm is probably a more appropriate pre-training. In this paper, we introduce MAE and verify the effect of visible patches for histopathological image understanding. Moreover, a novel SD-MAE model is proposed to enable a self-distillation augmented MAE. Besides the reconstruction loss on masked image patches, SD-MAE further imposes the self-distillation loss on visible patches to enhance the representational capacity of the encoder located shallow layer. We apply SD-MAE to histopathological image classification, cell segmentation and object detection. Experiments demonstrate that SD-MAE shows highly competitive performance when compared with other SSL methods in these tasks.
